Mixture of experts: a literature survey

  title={Mixture of experts: a literature survey},
  author={S. Masoudnia and R. Ebrahimpour},
  journal={Artificial Intelligence Review},
  • S. Masoudnia, R. Ebrahimpour
  • Published 2012
  • Computer Science
  • Artificial Intelligence Review
  • Mixture of experts (ME) is one of the most popular and interesting combining methods, which has great potential to improve performance in machine learning. ME is established based on the divide-and-conquer principle in which the problem space is divided between a few neural network experts, supervised by a gating network. In earlier works on ME, different strategies were developed to divide the problem space between the experts. To survey and analyse these methods more clearly, we present a… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Mixture of feature specified experts
    • 11
    Root-quatric mixture of experts for complex classification problems
    • 9
    • Highly Influenced
    When Gaussian Process Meets Big Data: A Review of Scalable GPs
    • 84
    • Highly Influenced
    • PDF
    Ensemble learning: A survey
    • 157
    Research and development of neural network ensembles: a survey
    • 24
    Mixture of Convolutional Neural Networks for Image Classification
    • 2018
    • 2
    • PDF


    Publications referenced by this paper.
    Adaptive Mixtures of Local Experts
    • 3,533
    • Highly Influential
    • PDF
    Improved learning algorithms for mixture of experts in multiclass classification
    • 129
    Ensemble based systems in decision making
    • 2,047
    • PDF
    Boosted Mixture of Experts: An Ensemble Learning Scheme
    • 108
    • PDF
    Switching between selection and fusion in combining classifiers: an experiment
    • 371
    • PDF
    Machine learning: a review of classification and combining techniques
    • 622
    • PDF
    On Combining Classifiers
    • 5,524
    • PDF
    Hierarchical mixtures of experts and the EM algorithm
    • 1,985
    • PDF
    Combining Predictors: Comparison of Five Meta Machine Learning Methods
    • 43